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In the local search, we varied the ammonium concentration gradually (from the starting condition which defines the reference values of the 5 variables) step by step and identified as the minimal solution in each step that minimized its squared distance Z with the previous solution.
Minimality and uniqueness: The functions should be the unique minimal solution in their interpolation class.
We say that x* is the least (respectively the greatest) solution of (1) in Y if x* ≤ x (respectively x* ≥ x) for any other solution x ∈ Y. Notice that the least solution in a subset Y is a minimal solution in Y, but the converse is false in general, and an analgous remark is true for maximal and greatest solutions.
A solution u* in A ⊂ S is a maximal solution in the set A if u* ≥ u for any other solution u ∈ A. The minimal solution in A is defined analogously by reversing the inequalities; when both a minimal and a maximal solution in A exist, we call them the extremal solutions in A. Next we pose our main result.
To search for the "minimal solution
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Then problem (1) has maximal and minimal solutions in.
Assume that conditions (S1)–(S5) hold, then (4.2) has maximal and minimal solutions in.
As conditions (H2) and (H3) are also satisfied (take, for example, ψ ≡ 1) we deduce that problem (1) has maximal and minimal solutions in.
For our approach to robustness of uncertain multi-objective optimization problems, minimal solutions in the sense of Definition 4 are useful and therefore, when considering robustness concepts, we will deal with this solution concept in the following.
Minimal solution, i.e. the smallest solution found by any software included in the study for a specific tree pair.
For each individual dimension, power and area, a single minimal solution exists in the design space, while both together consequently define the range of the solution space.
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